28 research outputs found
Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer
Wireless Power Transfer (WPT) is expected to be a technology reshaping the
landscape of low-power applications such as the Internet of Things, Radio
Frequency identification (RFID) networks, etc. Although there has been some
progress towards multi-antenna multi-sine WPT design, the large-scale design of
WPT, reminiscent of massive MIMO in communications, remains an open challenge.
In this paper, we derive efficient multiuser algorithms based on a
generalizable optimization framework, in order to design transmit sinewaves
that maximize the weighted-sum/minimum rectenna output DC voltage. The study
highlights the significant effect of the nonlinearity introduced by the
rectification process on the design of waveforms in multiuser systems.
Interestingly, in the single-user case, the optimal spatial domain beamforming,
obtained prior to the frequency domain power allocation optimization, turns out
to be Maximum Ratio Transmission (MRT). In contrast, in the general weighted
sum criterion maximization problem, the spatial domain beamforming optimization
and the frequency domain power allocation optimization are coupled. Assuming
channel hardening, low-complexity algorithms are proposed based on asymptotic
analysis, to maximize the two criteria. The structure of the asymptotically
optimal spatial domain precoder can be found prior to the optimization. The
performance of the proposed algorithms is evaluated. Numerical results confirm
the inefficiency of the linear model-based design for the single and multi-user
scenarios. It is also shown that as nonlinear model-based designs, the proposed
algorithms can benefit from an increasing number of sinewaves.Comment: Accepted to IEEE Transactions on Signal Processin
General Rank Multiuser Downlink Beamforming With Shaping Constraints Using Real-valued OSTBC
In this paper we consider optimal multiuser downlink beamforming in the
presence of a massive number of arbitrary quadratic shaping constraints. We
combine beamforming with full-rate high dimensional real-valued orthogonal
space time block coding (OSTBC) to increase the number of beamforming weight
vectors and associated degrees of freedom in the beamformer design. The
original multi-constraint beamforming problem is converted into a convex
optimization problem using semidefinite relaxation (SDR) which can be solved
efficiently. In contrast to conventional (rank-one) beamforming approaches in
which an optimal beamforming solution can be obtained only when the SDR
solution (after rank reduction) exhibits the rank-one property, in our approach
optimality is guaranteed when a rank of eight is not exceeded. We show that our
approach can incorporate up to 79 additional shaping constraints for which an
optimal beamforming solution is guaranteed as compared to a maximum of two
additional constraints that bound the conventional rank-one downlink
beamforming designs. Simulation results demonstrate the flexibility of our
proposed beamformer design
An Iterative Approach to Nonconvex QCQP with Applications in Signal Processing
This paper introduces a new iterative approach to solve or to approximate the solutions of the nonconvex quadratically constrained quadratic programs (QCQP). First, this constrained problem is transformed to an unconstrained problem using a specialized penalty-based method. A tight upper-bound for the alternative unconstrained objective is introduced. Then an efficient minimization approach to the alternative unconstrained objective is proposed and further studied. The proposed approach involves power iterations and minimization of a convex scalar function in each iteration, which are computationally fast. The important design problem of multigroup multicast beamforming is formulated as a nonconvex QCQP and solved using the proposed method
Non-convex Quadratically Constrained Quadratic Programming: Hidden Convexity, Scalable Approximation and Applications
University of Minnesota Ph.D. dissertation. September 2017. Major: Electrical Engineering. Advisor: Nicholas Sidiropoulos. 1 computer file (PDF); viii, 85 pages.Quadratically Constrained Quadratic Programming (QCQP) constitutes a class of computationally hard optimization problems that have a broad spectrum of applications in wireless communications, networking, signal processing, power systems, and other areas. The QCQP problem is known to be NP–hard in its general form; only in certain special cases can it be solved to global optimality in polynomial-time. Such cases are said to be convex in a hidden way, and the task of identifying them remains an active area of research. Meanwhile, relatively few methods are known to be effective for general QCQP problems. The prevailing approach of Semidefinite Relaxation (SDR) is computationally expensive, and often fails to work for general non-convex QCQP problems. Other methods based on Successive Convex Approximation (SCA) require initialization from a feasible point, which is NP-hard to compute in general. This dissertation focuses on both of the above mentioned aspects of non-convex QCQP. In the first part of this work, we consider the special case of QCQP with Toeplitz-Hermitian quadratic forms and establish that it possesses hidden convexity, which makes it possible to obtain globally optimal solutions in polynomial-time. The second part of this dissertation introduces a framework for efficiently computing feasible solutions of general quadratic feasibility problems. While an approximation framework known as Feasible Point Pursuit-Successive Convex Approximation (FPP-SCA) was recently proposed for this task, with considerable empirical success, it remains unsuitable for application on large-scale problems. This work is primarily focused on speeding and scaling up these approximation schemes to enable dealing with large-scale problems. For this purpose, we reformulate the feasibility criteria employed by FPP-SCA for minimizing constraint violations in the form of non-smooth, non-convex penalty functions. We demonstrate that by employing judicious approximation of the penalty functions, we obtain problem formulations which are well suited for the application of first-order methods (FOMs). The appeal of using FOMs lies in the fact that they are capable of efficiently exploiting various forms of problem structure while being computationally lightweight. This endows our approximation algorithms the ability to scale well with problem dimension. Specific applications in wireless communications and power grid system optimization considered to illustrate the efficacy of our FOM based approximation schemes. Our experimental results reveal the surprising effectiveness of FOMs for this class of hard optimization problems